Lab 7: Data Tidying, Transformation and Visualization with COVID-19 reporting data

Author

Dylan Mackler

library(tidyverse)
library(lubridate)

Loading data from a github repository

if (!dir.exists("data")) dir.create("data")
download.file(
  url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
  destfile = "data/time_series_covid19_confirmed_global.csv"
)
time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv") |>
  rename(Province_State = "Province/State", Country_Region = "Country/Region")

Data Tidying - Pivoting

time_series_confirmed_long <- time_series_confirmed |> 
               pivot_longer(-c(Province_State, Country_Region, Lat, Long),
                            names_to = "Date", values_to = "Confirmed") 

Dates and time

time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)

Making Graphs from the time series data

time_series_confirmed_long|> 
  group_by(Country_Region, Date) |> 
  summarise(Confirmed = sum(Confirmed)) |> 
  filter (Country_Region == "US") |> 
  ggplot(aes(x = Date,  y = Confirmed)) + 
    geom_point() +
    geom_line() +
    ggtitle("US COVID-19 Confirmed Cases")

time_series_confirmed_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("China","France","Italy", 
                                "Korea, South", "US")) |> 
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

time_series_confirmed_long_daily <- time_series_confirmed_long |>
  group_by(Country_Region, Date) |>
  summarise(Confirmed = sum(Confirmed), .groups = "drop") |>
  mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed)))
time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_point() +
      ggtitle("COVID-19 Confirmed Cases")

time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_smooth() +
      ggtitle("COVID-19 Confirmed Cases")

time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_smooth(method = "gam", se = FALSE) +
      ggtitle("COVID-19 Confirmed Cases")

Animated Graphs with gganimate

library(ggplot2)
library(gganimate)
library(gifski)
theme_set(theme_bw())
daily_counts <- time_series_confirmed_long_daily |> 
      filter (Country_Region == "US")

p <- ggplot(daily_counts, aes(x = Date,  y = Daily, color = Country_Region)) + 
        geom_point() +
        ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines  
        geom_point(aes(group = seq_along(Date))) +
        transition_reveal(Date) 

# make the animation
 animate(p, renderer = gifski_renderer(), end_pause = 15)

anim_save("daily_counts_US.gif", p)
# This download may take about 5 minutes. You only need to do this once.
download.file(
  url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
  destfile = "data/time_series_covid19_deaths_global.csv"
)
time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
  rename(Province_State = "Province/State", Country_Region = "Country/Region")

time_series_deaths_long <- time_series_deaths_confirmed |> 
    pivot_longer(-c(Province_State, Country_Region, Lat, Long),
        names_to = "Date", values_to = "Confirmed") 

time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)
p <- time_series_deaths_long |>
  filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
  ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) + 
    geom_point(aes(size=Confirmed)) + 
    transition_time(Date) + 
    labs(title = "Cumulative Deaths: {frame_time}") + 
    ylab("Deaths") +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)

Exercises

Exercise 1

# Compute rate per 10,000
table1 |>
  mutate(rate = cases / population * 10000)
# A tibble: 6 × 5
  country      year  cases population  rate
  <chr>       <dbl>  <dbl>      <dbl> <dbl>
1 Afghanistan  1999    745   19987071 0.373
2 Afghanistan  2000   2666   20595360 1.29 
3 Brazil       1999  37737  172006362 2.19 
4 Brazil       2000  80488  174504898 4.61 
5 China        1999 212258 1272915272 1.67 
6 China        2000 213766 1280428583 1.67 
# Compute total cases per year
table1 |> 
  group_by(year) |> 
  summarize(total_cases = sum(cases))
# A tibble: 2 × 2
   year total_cases
  <dbl>       <dbl>
1  1999      250740
2  2000      296920
# Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
  geom_line(aes(group = country), color = "grey50") +
  geom_point(aes(color = country, shape = country)) +
  scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000

5.2.1 Exercises

1.

1: observation - rate per 10000 people

2: observation - total cases per year

3: observations - case changes from 1999 to 2000 in Afghanistan, Brazil, and China

country = name of the country

year = year of the observation

cases= number of TB cases reported

population = total population of the country that year

rate = TB cases per 10,000 people (cases/population * 10000)

2. Sketching the Rate Calculation for table2 and table3

  • a. Extract TB cases per country-year
-   From `table2` and `table3`, reshape it to long format: one row per country-year
  • b. Join the two tables

    • Match on country and year to align cases and population
  • c. Calculate rate

    • Use mutate(rate = cases / population * 10000)
  • d. Store the result

    • Either as a new column in the joined table or as a new tibble

5.3.1 Data in column names

billboard
# A tibble: 317 × 79
   artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7   wk8
   <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99    NA
 2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA    NA
 3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54    53
 4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55    59
 5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36    49
 6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2     2
 7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA    NA
 8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35    38
 9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16    14
10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61    58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
#   wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
#   wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
#   wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
#   wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
#   wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …
billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank"
  )
# A tibble: 24,092 × 5
   artist track                   date.entered week   rank
   <chr>  <chr>                   <date>       <chr> <dbl>
 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk1      87
 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk2      82
 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk3      72
 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk4      77
 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk5      87
 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk6      94
 7 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk7      99
 8 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk8      NA
 9 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk9      NA
10 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk10     NA
# ℹ 24,082 more rows
billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank",
    values_drop_na = TRUE
  )
# A tibble: 5,307 × 5
   artist  track                   date.entered week   rank
   <chr>   <chr>                   <date>       <chr> <dbl>
 1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87
 2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82
 3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72
 4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77
 5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87
 6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94
 7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99
 8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91
 9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87
10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92
# ℹ 5,297 more rows
billboard_longer <- billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank",
    values_drop_na = TRUE
  ) |> 
  mutate(
    week = parse_number(week)
  )
billboard_longer |> 
  ggplot(aes(x = week, y = rank, group = track)) + 
  geom_line(alpha = 0.25) + 
  scale_y_reverse()

5.3.2 How does pivoting work?

df <- tribble(
  ~id,  ~bp1, ~bp2,
   "A",  100,  120,
   "B",  140,  115,
   "C",  120,  125
)
df |> 
  pivot_longer(
    cols = bp1:bp2,
    names_to = "measurement",
    values_to = "value"
  )
# A tibble: 6 × 3
  id    measurement value
  <chr> <chr>       <dbl>
1 A     bp1           100
2 A     bp2           120
3 B     bp1           140
4 B     bp2           115
5 C     bp1           120
6 C     bp2           125

5.3.3 Many variables in column names

who2
# A tibble: 7,240 × 58
   country      year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
   <chr>       <dbl>    <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
 1 Afghanistan  1980       NA        NA        NA        NA        NA        NA
 2 Afghanistan  1981       NA        NA        NA        NA        NA        NA
 3 Afghanistan  1982       NA        NA        NA        NA        NA        NA
 4 Afghanistan  1983       NA        NA        NA        NA        NA        NA
 5 Afghanistan  1984       NA        NA        NA        NA        NA        NA
 6 Afghanistan  1985       NA        NA        NA        NA        NA        NA
 7 Afghanistan  1986       NA        NA        NA        NA        NA        NA
 8 Afghanistan  1987       NA        NA        NA        NA        NA        NA
 9 Afghanistan  1988       NA        NA        NA        NA        NA        NA
10 Afghanistan  1989       NA        NA        NA        NA        NA        NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
#   sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
#   sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
#   sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
#   sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
#   sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …
who2 |> 
  pivot_longer(
    cols = !(country:year),
    names_to = c("diagnosis", "gender", "age"), 
    names_sep = "_",
    values_to = "count"
  )
# A tibble: 405,440 × 6
   country      year diagnosis gender age   count
   <chr>       <dbl> <chr>     <chr>  <chr> <dbl>
 1 Afghanistan  1980 sp        m      014      NA
 2 Afghanistan  1980 sp        m      1524     NA
 3 Afghanistan  1980 sp        m      2534     NA
 4 Afghanistan  1980 sp        m      3544     NA
 5 Afghanistan  1980 sp        m      4554     NA
 6 Afghanistan  1980 sp        m      5564     NA
 7 Afghanistan  1980 sp        m      65       NA
 8 Afghanistan  1980 sp        f      014      NA
 9 Afghanistan  1980 sp        f      1524     NA
10 Afghanistan  1980 sp        f      2534     NA
# ℹ 405,430 more rows

5.3.4 Data and variable names in the column headers

household
# A tibble: 5 × 5
  family dob_child1 dob_child2 name_child1 name_child2
   <int> <date>     <date>     <chr>       <chr>      
1      1 1998-11-26 2000-01-29 Susan       Jose       
2      2 1996-06-22 NA         Mark        <NA>       
3      3 2002-07-11 2004-04-05 Sam         Seth       
4      4 2004-10-10 2009-08-27 Craig       Khai       
5      5 2000-12-05 2005-02-28 Parker      Gracie     
household |> 
  pivot_longer(
    cols = !family, 
    names_to = c(".value", "child"), 
    names_sep = "_", 
    values_drop_na = TRUE
  )
# A tibble: 9 × 4
  family child  dob        name  
   <int> <chr>  <date>     <chr> 
1      1 child1 1998-11-26 Susan 
2      1 child2 2000-01-29 Jose  
3      2 child1 1996-06-22 Mark  
4      3 child1 2002-07-11 Sam   
5      3 child2 2004-04-05 Seth  
6      4 child1 2004-10-10 Craig 
7      4 child2 2009-08-27 Khai  
8      5 child1 2000-12-05 Parker
9      5 child2 2005-02-28 Gracie

5.4 Widening data

cms_patient_experience
# A tibble: 500 × 5
   org_pac_id org_nm                           measure_cd measure_title prf_rate
   <chr>      <chr>                            <chr>      <chr>            <dbl>
 1 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       63
 2 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       87
 3 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       86
 4 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       57
 5 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       85
 6 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       24
 7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       59
 8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       85
 9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       63
# ℹ 490 more rows
cms_patient_experience |> 
  distinct(measure_cd, measure_title)
# A tibble: 6 × 2
  measure_cd   measure_title                                                    
  <chr>        <chr>                                                            
1 CAHPS_GRP_1  CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…
2 CAHPS_GRP_2  CAHPS for MIPS SSM: How Well Providers Communicate               
3 CAHPS_GRP_3  CAHPS for MIPS SSM: Patient's Rating of Provider                 
4 CAHPS_GRP_5  CAHPS for MIPS SSM: Health Promotion and Education               
5 CAHPS_GRP_8  CAHPS for MIPS SSM: Courteous and Helpful Office Staff           
6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources             
cms_patient_experience |> 
  pivot_wider(
    names_from = measure_cd,
    values_from = prf_rate
  )
# A tibble: 500 × 9
   org_pac_id org_nm           measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
   <chr>      <chr>            <chr>               <dbl>       <dbl>       <dbl>
 1 0446157747 USC CARE MEDICA… CAHPS for MI…          63          NA          NA
 2 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          87          NA
 3 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          86
 4 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 5 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 6 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 7 0446162697 ASSOCIATION OF … CAHPS for MI…          59          NA          NA
 8 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          85          NA
 9 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          NA          83
10 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          NA          NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>
cms_patient_experience |> 
  pivot_wider(
    id_cols = starts_with("org"),
    names_from = measure_cd,
    values_from = prf_rate
  )
# A tibble: 95 × 8
   org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
   <chr>      <chr>        <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1 0446157747 USC C…          63          87          86          57          85
 2 0446162697 ASSOC…          59          85          83          63          88
 3 0547164295 BEAVE…          49          NA          75          44          73
 4 0749333730 CAPE …          67          84          85          65          82
 5 0840104360 ALLIA…          66          87          87          64          87
 6 0840109864 REX H…          73          87          84          67          91
 7 0840513552 SCL H…          58          83          76          58          78
 8 0941545784 GRITM…          46          86          81          54          NA
 9 1052612785 COMMU…          65          84          80          58          87
10 1254237779 OUR L…          61          NA          NA          65          NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>

5.4.1 How does pivot_wider() work?

df <- tribble(
  ~id, ~measurement, ~value,
  "A",        "bp1",    100,
  "B",        "bp1",    140,
  "B",        "bp2",    115, 
  "A",        "bp2",    120,
  "A",        "bp3",    105
)
df |> 
  pivot_wider(
    names_from = measurement,
    values_from = value
  )
# A tibble: 2 × 4
  id      bp1   bp2   bp3
  <chr> <dbl> <dbl> <dbl>
1 A       100   120   105
2 B       140   115    NA
df |> 
  distinct(measurement) |> 
  pull()
[1] "bp1" "bp2" "bp3"
df |> 
  select(-measurement, -value) |> 
  distinct()
# A tibble: 2 × 1
  id   
  <chr>
1 A    
2 B    
df |> 
  select(-measurement, -value) |> 
  distinct() |> 
  mutate(x = NA, y = NA, z = NA)
# A tibble: 2 × 4
  id    x     y     z    
  <chr> <lgl> <lgl> <lgl>
1 A     NA    NA    NA   
2 B     NA    NA    NA   
df <- tribble(
  ~id, ~measurement, ~value,
  "A",        "bp1",    100,
  "A",        "bp1",    102,
  "A",        "bp2",    120,
  "B",        "bp1",    140, 
  "B",        "bp2",    115
)
df |>
  pivot_wider(
    names_from = measurement,
    values_from = value
  )
# A tibble: 2 × 3
  id    bp1       bp2      
  <chr> <list>    <list>   
1 A     <dbl [2]> <dbl [1]>
2 B     <dbl [1]> <dbl [1]>
df |> 
  group_by(id, measurement) |> 
  summarize(n = n(), .groups = "drop") |> 
  filter(n > 1)
# A tibble: 1 × 3
  id    measurement     n
  <chr> <chr>       <int>
1 A     bp1             2

Exercise 2

ggplot(daily_counts, aes(x = Date, y = Daily)) +
  geom_line(color = "steelblue") +
  ggtitle("Daily Confirmed COVID-19 Cases by Country") +
  facet_wrap(~ Country_Region, scales = "free_y") +
  theme_minimal()

Exercise 3

selected_countries <- c("US", "India", "Brazil", "Germany", "South Africa")

daily_counts_subset <- time_series_confirmed_long_daily |>
  filter(Country_Region %in% selected_countries)

ggplot(daily_counts_subset, aes(x = Date, y = Daily, color = Country_Region)) +
  geom_line(size = 0.6, alpha = 0.8) +
  labs(
    title = "Daily Confirmed COVID-19 Cases",
    subtitle = "Selected Countries: US, India, Brazil, Germany, South Africa",
    x = "Date",
    y = "Daily Cases",
    color = "Country"
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 10)
  )

Exercise 4

download.file(
  url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
  destfile = "data/time_series_covid19_deaths_global.csv"
)
deaths_raw <- read_csv("data/time_series_covid19_deaths_global.csv")

deaths_long <- deaths_raw |>
  pivot_longer(cols = starts_with("1") | starts_with("2"), names_to = "Date", values_to = "Deaths") |>
  mutate(Date = as.Date(Date, format = "%m/%d/%y")) |>
  group_by(`Country/Region`, Date) |>
  summarise(Deaths = sum(Deaths), .groups = "drop") |>
  rename(Country_Region = `Country/Region`)
selected_countries <- c("US", "Canada", "Mexico")

deaths_subset <- deaths_long |>
  filter(Country_Region %in% selected_countries)
ggplot(deaths_subset, aes(x = Date, y = Deaths, color = Country_Region)) +
  geom_line(size = 0.8) +
  labs(
    title = "Cumulative COVID-19 Deaths",
    subtitle = "US, Canada, and Mexico",
    x = "Date",
    y = "Total Deaths",
    color = "Country"
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 10)
  )

Exercise 5

deaths_raw <- read_csv("data/time_series_covid19_deaths_global.csv")

deaths_long <- deaths_raw |>
  pivot_longer(cols = starts_with("1") | starts_with("2"), names_to = "Date", values_to = "Deaths") |>
  mutate(Date = as.Date(Date, format = "%m/%d/%y")) |>
  group_by(`Country/Region`, Date) |>
  summarise(Deaths = sum(Deaths), .groups = "drop") |>
  rename(Country_Region = `Country/Region`) |>
  arrange(Country_Region, Date)

deaths_daily <- deaths_long |>
  group_by(Country_Region) |>
  mutate(Daily_Deaths = Deaths - lag(Deaths, default = first(Deaths))) |>
  ungroup()

selected_countries <- c("US", "India", "Brazil", "Germany", "South Africa")

deaths_subset <- deaths_daily |>
  filter(Country_Region %in% selected_countries)

ggplot(deaths_subset, aes(x = Date, y = Daily_Deaths, color = Country_Region)) +
  geom_line(size = 0.7, alpha = 0.85) +
  labs(
    title = "Daily COVID-19 Deaths",
    subtitle = "US, India, Brazil, Germany, South Africa",
    x = "Date",
    y = "Deaths per Day",
    color = "Country"
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(size = 10)
  )

Exercise 6

library(ggplot2)
library(gganimate)

us_daily <- time_series_confirmed_long_daily |>
  filter(Country_Region == "US")

p <- ggplot(us_daily, aes(x = Date, y = Daily)) +
  geom_col(fill = "steelblue") +
  labs(
    title = "Daily Confirmed COVID-19 Cases in the US",
    x = "Date",
    y = "Cases per Day"
  ) +
  coord_cartesian(ylim = c(0, 1000000)) +
  theme_minimal() +
  transition_time(Date) +
  shadow_mark(past = TRUE, future = FALSE)

animate(p, renderer = gifski_renderer(), end_pause = 15)

anim_save("daily_confirmed_US.gif", p)